Taming uncertainty in distribution grid planning – A scenario-based methodology for the analysis of impact of electric vehicles.

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Paper number
1955
Working Group Number
Conference name
CIRED 2019
Conference date
3-6 June 2019
Conference location
Madrid, Spain
Peer-reviewed
Yes
Short title
Convener
Authors
Toffanin, Damiano, Adaptricity AG, Switzerland
Ulbig, Andreas, Adaptricity AG, Switzerland
Abstract
Investigating grid adequacy for future scenarios of electricity demand is a continuing concern among distribution grid planners and power system researchers. The wide-spread adoption of plug-in electric vehicles (PEVs) is connected with increased risks of line overloading or undervoltage events, because of the significant additional load associated at traditional peak loading times and a high coincidence factor of these new load types.Understanding the complexity of ensuring long-term adequacy of distribution grids, given the uncertainty associated to such game-changing trends, is vitally important to design reliable methods. This paper presents a scenario-based grid analysis and grid planning method using Monte Carlo simulations, that quantifies the likelihood of critical grid situations and can be used by grid planners to target actions such as grid upgrades and grid reconfigurations in advance. Overall, the aim is to achieve better cost-efficiency in grid planning by taming, e.g. reducing, planning uncertainties caused by changing grid usage patterns. The methodology can be applied to both medium- and low-voltage grids.The scenario-based grid planning method is designed to focus on voltage violation and component overloading (i.e. lines and transformers), given an uncoordinated penetration of new appliance types in the form of PEVs. The results can then be exploited as a starting point for the implementation of countermeasures.
Table of content
Keywords
Publisher
AIM
Date
2019-06-03
Permanent link to this record
https://cired-repository.org/handle/20.500.12455/660
http://dx.doi.org/10.34890/882
ISSN
2032-9644
ISBN
978-2-9602415-0-1